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Bertrand Thirion

Researcher at Université Paris-Saclay

Publications -  334
Citations -  91237

Bertrand Thirion is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Cluster analysis & Cognition. The author has an hindex of 51, co-authored 311 publications receiving 73839 citations. Previous affiliations of Bertrand Thirion include French Institute for Research in Computer Science and Automation & French Institute of Health and Medical Research.

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Journal Article

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, +15 more
- 01 Feb 2011 - 
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Scikit-learn: Machine Learning in Python

Fabian Pedregosa, +18 more
- 02 Jan 2012 - 
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI

Machine learning for neuroimaging with scikit-learn.

Alexandre Abraham, +16 more
- 21 Feb 2014 - 
TL;DR: It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.
Journal ArticleDOI

Assessing and tuning brain decoders: cross-validation, caveats, and guidelines

Gaël Varoquaux, +5 more
- 15 Jan 2017 - 
TL;DR: T theory and experiments outline that the popular “leave‐one‐out” strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred, and it can be favorable to use sane defaults, in particular for non‐sparse decoders.
Journal ArticleDOI

Variability in the analysis of a single neuroimaging dataset by many teams

Rotem Botvinik-Nezer, +220 more
- 04 Jun 2020 - 
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.